enh: multiple masks for compcor #1968
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@@ Coverage Diff @@
## master #1968 +/- ##
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+ Coverage 72.6% 72.6% +<.01%
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Files 1070 1070
Lines 54454 54530 +76
Branches 7868 7892 +24
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+ Hits 39535 39591 +56
- Misses 13686 13699 +13
- Partials 1233 1240 +7
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nipype/algorithms/confounds.py
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| @@ -434,7 +441,7 @@ class TCompCorInputSpec(CompCorInputSpec): | |||
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| class TCompCorOutputSpec(CompCorInputSpec): | |||
| # and all the fields in CompCorInputSpec | |||
| high_variance_mask = File(exists=True, desc="voxels excedding the variance threshold") | |||
| high_variance_mask = InputMultiPath(File(exists=True, desc="voxels excedding the variance threshold")) | |||
nipype/algorithms/confounds.py
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| @@ -302,7 +302,7 @@ def _list_outputs(self): | |||
| class CompCorInputSpec(BaseInterfaceInputSpec): | |||
| realigned_file = File(exists=True, mandatory=True, | |||
| desc='already realigned brain image (4D)') | |||
| mask_file = File(exists=True, desc='mask file that determines ROI (3D)') | |||
| mask_file = InputMultiPath(File(exists=True, desc='mask file(s) that determines ROI (3D)')) | |||
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how does this work for TCompCor? i see that only the first one is being used.
nipype/algorithms/confounds.py
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| @@ -464,7 +471,7 @@ def _run_interface(self, runtime): | |||
| .format(self.inputs.realigned_file, imgseries.ndim, imgseries.shape)) | |||
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| if isdefined(self.inputs.mask_file): | |||
| in_mask_data = nb.load(self.inputs.mask_file, mmap=NUMPY_MMAP).get_data() | |||
| in_mask_data = nb.load(self.inputs.mask_file[0], mmap=NUMPY_MMAP).get_data() | |||
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should we consider a union or intersection of masks, and a new Enum trait (for 'union', 'intersect') and xor-ed with an index trait if we want to index just one of the masks.
merge_method = traits.Enum('union', 'intersect', 'none', xor=['mask_index'], ...)
mask_index = traits.Range(0, low=0, xor=['merge_method'], ...)
none corresponds to per mask.
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if you can make these changes, we can get them in.
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@satra maybe you can take a look, there is a failing test I have to figure out first
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Still have to add some basic testing but LMKWYT |
nipype/algorithms/confounds.py
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| # save mask | ||
| mask_file = os.path.abspath('mask{}.nii'.format(i)) | ||
| nb.Nifti1Image(mask_data, nb.load( | ||
| self.inputs.realigned_file).affine).to_filename(mask_file) |
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this should also have the numpy MMAP
nipype/algorithms/confounds.py
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| # save mask | ||
| mask_file = os.path.abspath('mask.nii') | ||
| nb.Nifti1Image(mask_data, | ||
| nb.load(self.inputs.realigned_file).affine).to_filename(mask_file) |
nipype/algorithms/confounds.py
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| usedefault=True, | ||
| desc='filename to store physiological components') | ||
| desc='already realigned brain image (4D)') | ||
| mask_files = InputMultiPath(File(exists=True, |
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this should go through a deprecation cycle. so keep the old name and point to the new name.
e.g.: https://github.com/nipy/nipype/blob/master/nipype/interfaces/spm/preprocess.py#L812
allows multiple masks to be passed in - outputs are stacked in the same components file